Reducing Selection Bias in Analyzing Longitudinal Health Data with High Mortality Rates

Authors

Xian Liu, Uniformed Services University of the Health Sciences, Bethesda MD and Walter Reed National Military Medical Center, Bethesda MDFollow
Charles C. Engel, Uniformed Services University of the Health Sciences, Bethesda MD and Walter Reed National Military Medical Center, Bethesda MDFollow
Han Kang, Department of Veterans AffairsFollow
Kristie L. Gore, Walter Reed National MilitaryFollow

Abstract

Two longitudinal regression models, one parametric and one nonparametric, are developed to reduce selection bias when analyzing longitudinal health data with high mortality rates. The parametric mixed model is a two-step linear regression approach, whereas the nonparametric mixed-effects regression model uses a retransformation method to handle random errors across time.

DOI

10.22237/jmasm/1288584480